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Published February 20, 2022 | Submitted + Published
Journal Article Open

Substructure at High Speed. II. The Local Escape Velocity and Milky Way Mass with Gaia eDR3

Abstract

Measuring the escape velocity of the Milky Way is critical in obtaining the mass of the Milky Way, understanding the dark matter velocity distribution, and building the dark matter density profile. In Necib & Lin, we introduced a strategy to robustly measure the escape velocity. Our approach takes into account the presence of kinematic substructures by modeling the tail of the stellar distribution with multiple components, including the stellar halo and the debris flow called the Gaia Sausage (Enceladus). In doing so, we can test the robustness of the escape velocity measurement for different definitions of the "tail" of the velocity distribution and the consistency of the data with different underlying models. In this paper, we apply this method to the Gaia eDR3 data release and find that a model with two components is preferred, although results from a single-component fit are also consistent. Based on a fit to retrograde data with two bound components to account for the relaxed halo and the Gaia Sausage, we find the escape velocity of the Milky Way at the solar position to be v_(esc) = 445⁺²⁵₋₈ km s⁻¹. A fit with a single component to the same data gives v_(esc) = 472⁺¹⁷₋₁₂ km s⁻¹. Assuming a Navarro−Frenck−White dark matter profile, we find a Milky Way concentration of c₂₀₀ = 19⁺¹¹₋₇ and a mass of M₂₀₀ = 4.6^(+1.5)_(-0.8) x 10¹¹M⊙, which is considerably lighter than previous measurements.

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 February 23; revised 2021 November 5; accepted 2021 December 11; published 2022 February 24. We are grateful to I. Moult for early discussions and collaboration on the project and to M. Lisanti for helpful feedback. We would also like to thank L. Anderson, A. Bonaca, G. Collin, A. Deason, P. Hopkins, A. Ji, and J. Johnson for helpful conversations. This work was performed in part at Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1607611. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under contract No. DE-AC02-05CH11231. L.N. is supported by the DOE under award No. DESC0011632, the Sherman Fairchild fellowship, the University of California Presidential fellowship, and the fellowship of theoretical astrophysics at Carnegie Observatories. T.L. is supported by an Alfred P. Sloan Research Fellowship and Department of Energy (DOE) grant DE-SC0019195. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Software: Astropy (Astropy Collaboration et al. 2013, 2018), corner.py (Foreman-Mackey 2016), emcee (Foreman-Mackey et al. 2013), IPython (Pérez & Granger 2007), Galpy (Bovy 2015).

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Published - Necib_2022_ApJ_926_189.pdf

Submitted - 2102.02211.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023